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Combining shells and sequences to untangle taxonomy of abalone in Sabah, Malaysia
Get PDFAbalone, herbivorous marine mollusks of significant economic and ecological importance, exhibit considerable morphological plasticity. This poses a challenge for accurate species identification, which in turn could undermine the assessment of impacts from harvesting. The present study employed an integrative approach combining geometric morphometrics and DNA barcoding to address potential taxonomic ambiguities in abalone populations from Sabah, Malaysia. Especially in this megadiverse region, it could be expected that multiple species may co-occur. Morphometric analysis of 135 specimens, using 14 shell landmarks, confirmed that all individuals clustered within the Haliotis asinina group when compared with data from Haliotis glabra. This was supported by genetic analyses, which demonstrated 99% sequence similarity among novel CO1 sequences and previously published DNA barcodes from H. asinina. Despite overlapping morphological traits between H. asinina and similar congeners, the integrative approach conclusively identified all specimens as H. asinina. Although there are some limits to shell-based taxonomy, quantitative approaches to both morphological and genetic data can resolve species boundaries. These results underscore the importance of employing integrative methods in biodiversity assessments and conservation strategies for tropical abalone species
Sibling empathy among preschoolers in China: analyzing emotional responses and family influences
Get PDFIntroduction: Empathy between siblings plays a pivotal role in early socio-emotional development, yet limited research has explored this construct within the context of Chinese preschool-aged children, particularly in light of China’s changing family structures. This study addresses this gap by examining the characteristics of sibling empathy and its associations with general empathy and sibling relationship quality. Methods: A total of 222 children aged 3 to 6 years from two-child families in Zhejiang Province, China, participated in this study. Sibling empathy was assessed using the newly developed Measurement of Sibling Empathy in Chinese Preschool Children (MSCP). The study examined differences in sibling empathy across age, gender, birth order, and sibling gender combinations (i.e., two boys, two girls, and one boy and one girl). A mediation model was tested to evaluate the role of sibling empathy in linking general empathy to sibling relationship quality. Results: Analysis revealed that younger children exhibited significantly lower empathy for sadness, and second-born children showed higher empathy for fear. Two-girl sibling pairs demonstrated greater empathy for anger than mixed-gender pairs. No significant gender differences were observed. General empathy was positively associated with both sibling empathy and sibling relationship quality. Structural Equation Modeling (SEM) indicated that sibling empathy significantly mediated the relationship between general empathy and sibling relationship quality. Discussion: The findings contribute to developmental and cultural theories of empathy by highlighting emotion-specific variations in sibling empathy and their implications for sibling dynamics in Chinese families. While the cross-sectional design and reliance on mother-reported data pose limitations, this study offers foundational insights and points toward targeted interventions to foster empathy and improve sibling relationships in early childhood
Reduced default mode network functional connectivity correlates with stroke and uncontrolled hypertension among patients with Alzheimer’s disease: an fMRI study in Klang Valley Malaysia
Get PDFBackground: Risk factors for cardiovascular disease (CVD) have been increasingly implicated in the development of dementia as Alzheimer’s disease (AD) and tagged as Type III diabetes. Nevertheless, there is limited data on these metabolic effects upon the brain morphological changes and neuronal functional connectivity (FC). Our study was undertaken to predict the neuroimaging biomarkers of brain structural and functional abnormality with regards to CVD risk factors in AD patients compared to age-matched cognitively healthy controls (HC) in central Malaysia. Methodology: A phase I cross-sectional study of patients presenting with memory impairment and cognitive decline was conducted in the memory clinic at Hospital Kuala Lumpur (HKL). Sociodemographic data, neuropsychological test scores, and CVD risk factors were reviewed from medical records data from 2014 to 2019 and analysed based on patients diagnosed with various neurocognitive disorders. The second phase of the study involved recruiting AD subjects from HKL, Hospital Pengajar UPM and Klinik Kesihatan Pandamaran, Selangor as well as HC subjects from Klang Valley for a functional MRI study. Differences in CVD risks factors, grey matter volume (GMV) deficit, and neuronal FC were compared between AD and HC. Results: In phase I study, a total of 298 patients (30 MCI, and 268 dementia) were evaluated, with dementia patients consisting of 78 Alzheimer’s disease (AD), 93 Vascular dementia (VaD), 94 Mixed dementia, 2 early-onset Alzheimer’s disease (EOAD) and 1 Logopenic Progressive Aphasia type of AD (LPA). History of stroke was strongly associated with MCI and dementia (p=0.023). Hypertension was associated with diagnosis of AD but did not achieve statistical significance (p = 0.168). AD patients had reduced GMV compared to HC in the right medial temporal lobe, left fusiform, right medial occipital region right superior temporal lobe and right parahippocampal region (p-value < 0.05). There was significantly reduced functional connectivity in the default mode network of AD compared to HC. Conclusion: Hypertension, history of stroke, imaging biomarkers of reduced GMV at medial temporal lobe and decreased functional connectivity at DMN are predictors of Alzheimer’s disease among the patients in a central Malaysian population. Thus, monitoring these parameters and lifestyle modifications are recommended to alleviate these risk factors to delay the onset of AD
Analyzing activity of daily living data utilizing motor activity log toward quantitative scoring system
Get PDFAssessment of stroke severity and recovery progress relies on a therapist’s rating or score. It is typically administered manually with subjective input from therapists. This method is exposed to inconsistency, particularly when involving different therapists which depends on their own experiences and expertise. This paper presents a study on one-way ANOVA analysis to investigate the impact of force, forearm and elbow movement, Activity of Daily Living (ADL) equipment motion, and time duration on the MAL score during the execution of ADLs. A Motor Activity Log (MAL) is employed as the standard clinical assessment benchmark, where ten ADLs have been selected from the MAL standard for data collection purposes involving 30 healthy individuals and 56 stroke patients. The analyses are divided into two which are Analysis 1) focuses on the data with therapist rating 5, while Analysis 2) considers the data with therapist ratings ranging from 1 to 5. Data inputs including force, forearm and elbow movement, ADLs equipment motion, and activity time duration have been collected using sensors of force, distance, Inertial Measurement Unit (IMU), and encoders. Output data in MAL scores are obtained manually from therapists using the current methodology. The results indicate significant differences in 19 out of 40 cases for Analysis 1) and 85 out of 100 cases for Analysis 2). This paper contributes towards an objective and accurate automatic scoring system for a more consistent and efficient assessment of stroke patients’ performance and recovery progress
Enhancing campus mobility: simulated multi-objective optimization of electric vehicle sharing systems within an intelligent transportation system frameworks
Get PDFThis research optimizes an electric vehicle (EV) sharing system for a university campus, focusing on different demand patterns and peak times within an Intelligent Transportation System (ITS) framework. The main objectives are to reduce the number of unserved demands and operational costs. A simulation model was developed in MATLAB, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), a powerful multi-objective optimization technique that balances conflicting objectives to achieve the best trade-offs for operational efficiency. In addition to conventional decision variables, dynamic dual relocation thresholds and charge levels are introduced as decision variables to enhance optimization. The study compares two scenarios: Equally Distributed Demand (EDD) and Non-Equally Distributed Demand (NEDD), customized for the University Putra Malaysia (UPM) campus. Findings indicate that the NEDD scenario, which concentrates on specific demand areas, effectively decreases unserved demands and operational costs. Additionally, a station-specific approach expanded the solution space, improving adaptability and resulting in notable reductions in operational costs and smaller but meaningful improvements in unserved demands, especially during peak periods. By setting station-specific relocation thresholds and charge levels, resources were deployed efficiently, minimizing unnecessary relocations. The use of dynamic values for dual relocation thresholds and charge-to-work levels further optimized the process, reducing operational costs significantly, with a lesser impact on unserved demands across both scenarios. This research offers valuable insights into the implementation of EV sharing systems in educational institutions, emphasizing the advantages of focused resource allocation and the integration of dynamic decision variables
Toward automatic detection of pi2 magnetic pulsation using machine learning
Get PDFMagnetic pulsations of type Pi2 are a well-established category of Ultra Low Frequency (ULF) waves, characterized by irregularly damped oscillations with periods ranging from 40 to 150 seconds (6.7-25 mHz). Nowadays, it is well known that Pi2 occurs at the onset of geomagnetic substorms, is considered an outstanding research topic in space physics, and is a link between ionospheric and magnetospheric processes. The discontinuation of the conventional index previously employed to detect Pi2 pulsations has driven this study to propose an innovative detection method leveraging machine learning (ML). This paper introduces a novel ML-based classification framework that utilizes geomagnetic field data for Pi2 pulsation detection. A comprehensive analysis of various linear, ensemble, and non-linear ML models was conducted, employing hyperparameter optimization to identify the optimal model with high classification performance and minimal computational overhead during testing. Model robustness was assessed using multiple evaluation metrics, including accuracy, F1-score, kappa score, execution time, precision-recall curves, ROC curves, learning curves, and confusion matrices. The proposed gradient boost (GB) classifier demonstrated superior performance, achieving 98.21% accuracy in distinguishing Pi2 pulsations. This detection system offers a reliable and efficient tool for monitoring Pi2 pulsations in the nighttime, contributing to advancements in space weather analysis and substorm detection
Machine learning and spatio temporal analysis for assessing ecological impacts of the Billion Tree Afforestation Project
Get PDFThis study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel-2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high-confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data-driven afforestation efforts and inform sustainable environmental management practices
Feature selection techniques for enhancing app user review analysis
Get PDFThe rapid growth of user-generated content, particularly app user reviews, presents a significant challenge in analyzing and extracting useful insights. The unstructured nature, inconsistent quality, and large volume of these reviews make it difficult to identify relevant information for app maintenance and updates. This study addresses this challenge by evaluating the impact of different feature selection techniques on the performance of machine learning models in multi-label classification tasks for app user review analysis. Our findings indicate that the subset attributes derived from a combination of Information Gain, GINI Index, and Correlation Matrix can improve the performance of machine learning models. Using a Support Vector Machine with proposed innovative score-based zero shot technique, promising results were achieved on average with 96.75% precision, 69.39% recall, and 80.81% F1-Score. Additionally, high-quality features, such as the percentage Difference can enhance performance in multi-label classification tasks, providing valuable insights for practitioners and researchers. The research implications and significance highlight the practical applications and strategic value of these findings, contributing to the advancement of knowledge and practice in the field of software engineering, particularly for multi-label classification tasks
Redefining obesity in the Indonesian population: the critical role of waist-to-height ratio in screening for diabetes mellitus and hypertension
Get PDFObjectives: Waist-to-height ratio (WHtR) is an alternative index to evaluate metabolic health and predict the risk of estimating the impact of adiposity on cardiometabolic diseases. Despite the significance, the diagnostic performance of WHtR has not been extensively investigated in large epidemiological studies in Indonesia. Therefore, this study aimed to investigate anthropometric indexes (body mass index [BMI], waist circumference [WC], waist-to-hip ratio [WHR], and WHtR) with the best clinically accurate and diagnostic performance in detecting the prevalence of diabetes mellitus (DM) and hypertension (HTN) in the Indonesian population.
Methods: This study used a cross-sectional method to analyze big data of 7699 individuals from the Indonesian Family Life Survey. The diagnostic performance of each anthropometric index was analyzed using the receiver operating characteristics (ROC) curve model in the SPSS and MedCalc applications. Furthermore, the associations of anthropometric indexes with DM and HTN were evaluated using logistic regression adjusted for sociodemographic confounders.
Results: WHtR showed the highest area under the curve (AUC) for detecting DM in men (0.731 [0.679–0.784]), as well as HTN in both men (0.650 [0.629–0.671]) and women (0.615 [0.598–0.633]). Although often negligible, the discrepancies had overlapping 95% confidence intervals with other indexes. WHtR also showed the strongest association with both DM (AOR [95% CI]: 3.166 [2.416–4.150]) and HTN (1.938 [1.703–2.206]). Lower cutoffs for BMI (22.72 kg/m2) and WC (83.35 cm) enhanced sensitivity for DM and HTN detection, particularly in men.
Discussion: WHtR outperformed BMI, WC, and WHR in detecting DM and HTN in the Indonesian population. Additionally, lower cutoffs for overall (BMI) and abdominal obesity (WC) should be considered to enhance the sensitivity of anthropometric indexes in screening for cardiometabolic diseases in the population
Fabrication and characterization of single-wall carbon nanotube and its biocompatibility to human hepatocytes
Get PDFSingle-wall carbon nanotubes (SWCNTs) have emerged as promising nanocarriers for targeted cancer drug delivery due to their unique structural properties. However, their cytotoxicity remains a significant challenge, as the biocompatibility of SWCNTs with human cells, particularly hepatocytes, is crucial for their clinical application. The toxicity of SWCNTs is influenced by factors such as nanoparticle size, morphology, surface chemistry, and the presence of impurities. In this study, we aimed to synthesize highly pure SWCNTs and assess their biocompatibility with human hepatocyte cells. SWCNTs were fabricated using a modified chemical vapor deposition (CVD) method, followed by a two-step acid purification technique. Raman spectroscopy and electron microscopy confirmed a high purity level of 99.8 %. The biocompatibility of the purified SWCNTs was evaluated using an in vitro model with human hepatocytes. Results indicated that high concentrations of SWCNTs (>50 μg/ml) significantly reduced cell viability, increased lactate dehydrogenase leakage, and elevated lipid peroxidation, while simultaneously suppressing antioxidant enzyme activity. Flow cytometry analysis further revealed that exposure to high concentrations of SWCNTs induced apoptosis in hepatocytes. Molecular analysis of key biomarkers demonstrated upregulation of TNF-α, IL1β, NF-kB, and iNOS, alongside downregulation of nrf2 gene and protein expression. These alterations contribute to the mechanisms underlying SWCNT-induced oxidative stress and apoptosis in human hepatocyte cells. Despite the high purity of SWCNTs, their cytotoxic effects may be attributed to their inherent physical properties, including rigidity, surface area, and fiber length. In conclusion, while SWCNTs hold great potential for cancer drug delivery, managing their toxicity remains critical for their future therapeutic applications